Cloud based video detection and tracking system

ABSTRACT

A method for detecting and tracking multiple moving targets from airborne video within the framework of a cloud computing infrastructure. The invention simultaneously utilizes information from an optical flow generator and an active-learning histogram matcher in a complimentary manner so as to rule out erroneous data that may otherwise, separately, yield false target information. The invention utilizes user-based voice-to-text color feature description for track matching with hue features from image pixels.

STATEMENT OF GOVERNMENT INTEREST

The invention described herein may be manufactured and used by or forthe Government for governmental purposes without the payment of anyroyalty thereon.

BACKGROUND OF THE INVENTION

This invention relates generally to the field of cloud based videodetection and tracking systems. More specifically, this disclosurepresents the first attempt at detecting and tracking multiple movingtargets from an airborne video within the framework of a cloud computinginfrastructure for the application of suspicious behavior recognition.

The detection and tracking of moving objects is critical in many defenseand security applications, where motion detection is usually performedin a preprocessing step, a key success in the following of a targettracking and automatic target recognition. Many videos used in defenseand security applications are outdoor videos whose quality may bedegraded by various noisy sources, such as atmospheric turbulence,sensor platform scintillation, etc. Meanwhile, moving objects may bevery small occupying a few pixels only, which makes motion detectionvery challenging. Under this circumstance, existing approaches maygenerate significant amount of false alarms of detecting things that arenot targets.

Motion detection has been extensively investigated. Many research worksare conducted from indoor videos with large objects. As one of the majortechniques, optical flow based approaches have been widely used formotion detection. There are two classic methods of optical flowcomputation in computer vision: Gunnar Farneback (GF) method andLucas-Kanade (LK) method. Both of them are based on the two-framedifferential algorithms. Since the LK method needs the construction of apyramid model in sparse feature scale space and iterative computationalupdating at successively finer scales, a preferred approach would be tofocus on the GF method for a dense optical flow computation.

BRIEF SUMMARY OF THE INVENTION

Experiments conducted for video detection and tracking applicationsappearing in the literature mostly involve stable lighting conditionsand relatively stable image collections when using cameras. However, inpractice, image information captured under realistic lighting conditionsdegrades considerably under moving background and unstable imagecollections. As a result, target detection and tracking becomes quitechallenging and many false alarms get generated by existing approaches.In the present invention, a developed target detection and trackingsystem consists of cloud based image alignment, online target colorcalibration, optical flow detection and histogram matching.

OBJECTS AND SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a methodfor the detection and tracking of moving objects.

It is a further object of the present invention to provide a method forthe detection and tracking of moving objects that overcomes the priorart's limitations in generating false target alarms.

Briefly stated, the present invention achieves these and other methodsthrough the detection and tracking of multiple moving targets fromairborne video within the framework of a cloud computing infrastructure.The invention simultaneously utilizes information from an optical flowgenerator and an active-learning histogram matcher in a complimentarymanner so as to rule out erroneous data that may otherwise, separately,yield false target information. The invention utilizes user-basedvoice-to-text color feature description for track matching with huefeatures from image pixels.

According to an embodiment of the present invention, a method for videodetection and tracking of a target, comprising the steps of defining atarget by selecting a dataset of target image frames from a database andselecting the desired color of the target; converting the color to atemplate hue histogram representation: initializing an image detector;performing target image frame alignment and registration in whichhomography matrices are generated; producing an optical flow field;executing morphology processing on the optical flow field so as toproduce candidate target contours; matching the target contours to thetemplate hue histogram representation; initializing tracking of thetarget and generating target tracks; aligning a current target imageframe so as to form a sequential track of the target; and when thetarget is not located, redefining the target.

The above and other objects, features and advantages of the presentinvention will become apparent from the following description read inconjunction with the accompanying drawings, in which like referencenumerals designate the same elements.

REFERENCES

-   Bao, C., et al., “Real-time robust L1 tracker using accelerated    proximal gradient approach,” IEEE Conference on Computer Vision and    Pattern Recognition, Providence, R.I., 2012, pp. 1830-1837.-   Bay, H., et al., “SURF: Speeded Up Robust Features,” Computer Vision    and Image Understanding, vol. 110, no. 3, June 2008, pp. 346-359.-   Bruhn, A., et al., “Lucas/Kanade meets Horn/Schunck: combining local    and global optic flow methods,” International Journal of Computer    Vision, vol. 61, no. 3, 2005, pp. 211-231.-   Cloud optimized virtualization,    “http://www.citrix.com/products/xenserver/overview.html”-   Farneback, G., “Two-Frame motion estimation based on polynomial    expansion,” Image Analysis, vol. 2749, February 2003, pp. 363-370.-   Harris, C., et al., “A combined corner and edge detector,”    Proceedings of the Alvey Vision Conference, 1988. pp. 147-151.-   Horn, B., et al., “Determining optical flow”, Artificial    Intelligence, vol. 17, no. 1-3, 1981, pp. 185-203.-   Horn, B., et al., “Determining optical flow: a retrospective”,    Artificial Intelligence, vol. 59, no. 1-2, 1993. pp. 81-87.-   Hu, W., et al., “A survey on visual surveillance of object motion    and behaviors,” IEEE Transactions on Systems. Man, and    Cybernetics—Part C: Applications and Reviews, vol. 34, no. 3, August    2004, pp. 334-352.-   Liu, K., et al., “Real-time robust vision-based hand gesture    recognition using stereo images.” Journal of Real-Time Image    Processing, February 2013, pp. 1-9.-   Lowe, D., “Distinctive Image Features from Scale-Invariant    Keypoints,” International Journal of Computer Vision, Macau, vol.    60, no. 2, November 2004, pp. 91-110.-   Lucas, B. et al., “An iterative image registration technique with an    application to stereo vision,” Proceedings of International Joint    Conference on Artificial Intelligence, Vancouver, Canada, vol. 2,    1981, pp. 522.-   Mei, X., et al, “Minimum Error Bounded Efficient L1 Tracker with    Occlusion Detection,” IEEE Computer Vision and Pattern Recognition,    2011.-   Mitiche, A., et al., “Computation and analysis of image motion: a    synopsis of current problems and methods,” International Journal of    Computer Vision, vol. 19, no. 1, 1996, pp. 29-55.-   Senin, P., Dynamic Time Warping Algorithm Review, Technical Report,    Information and Computer Science Department, University of Hawaii at    Manoa, 2008.-   Wang, L., et al., “High-quality real-time stereo using adaptive cost    aggregation and dynamic programming,” Proceedings of IEEE    International Symposium on 3D Data Processing, Visualization, and    Transmission, Chapel Hill. N.C., 2006, pp. 798-805.-   Yilmaz, A., et al., “Object tracking: a survey,” ACM Computing    Surveys, vol. 38. no. 4, 2006, pp. 1-45.-   Zhang, K., et al., “Real-time compressive tracking,” European    Conference on Computer Vision, Florence, Italy, 2012, vol. 7574, pp.    864-877.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a typical template hue histogram in silver used fortarget matching. The color depth of hue component in HSV(hue-saturation-value) is set to 0 to 255 and they are divided into 16bars with the same step width.

FIG. 2 depicts a typical template hue histogram in red used for targetmatching

FIG. 3 shows a binary image including the strong candidate windows basedon the optical flow field in the frame. The window with the solid whiteboundary represents that it is a good match of the target in silver,while the one with the dashed white boundary represents that it is agood match of the target in red, and the one with the default dottedwhite boundary represents that it is not a good match with any of thetemplates.

FIG. 4 shows an original RGB (red-green-blue) image for the visiblereference to the system operator.

FIG. 5 depicts a flowchart that describes the main components and theprocess of the cloud based video detection and tracking system.

FIG. 6 shows an image with the detected targets and the registeredtracks.

FIG. 7 depicts a high level view of a host computer in the cloud system.

FIG. 8 shows the overall data flow of the tracking module.

FIG. 9 shows the web GUI (graphical user interface) when the usertriggers a voice command to track the silver car in the video sequence.

FIG. 10 shows an instance of the system monitoring module or thedashboard on the web GUI.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention utilizes a template hue histogram associated withspecific color in a window as a matching feature to locate or redetectthe target location estimate when its tracking is hampered by theenvironment. The candidate window with the histogram is generated fromthe optical flow field between two aligned consecutive frames. Anyoptical flow blob with a similar histogram will be considered as astrong potential candidate. The template hue histogram will be tunedaccording to the strong potential candidates. The goal of the onlinetarget color calibration is to adapt the present invention's subsequentcolor processing to the color characteristic of the light source underwhich images are captured. This technique has been previously used quitesuccessfully for hand detection in order to cope with unknown colorcharacteristics of various light sources encountered in practice. Thecalibration is performed from the beginning when the system is turned onor the tracked target is missing till the target is located orredetected. It involves updating the histogram in HSV color space torepresent the color characteristics of the target being captured in anonline or on-the-fly manner. The calibration is easily applied by aweighted linear addition between the template histogram and the strongcandidate histogram. Representative color pixels of the target arecollected within the window and the histogram is calibrated and used asa feature to perform target locating or redetection. Thus, the developedsolution is designed to be robust to different background and lightingconditions.

The present invention also utilizes a cloud infrastructure to run imagealignment or registration processes in parallel and thus a non-real-timealgorithm can perform at a real-time frame rate. The well-knownattractive features of cloud computing include on-demand scalability ofhighly available and reliable pooled computing resources, secure accessto metered services from anywhere, and displacement of data and servicesfrom inside to outside the organization.

In the present invention, the target track generation based on the imagealignment and registration enables further analysis and estimation tothe target behavior. The image alignment is a process of transformingdifferent sets of data into one coordinate system, since the homographymatrix generated from the image alignment can further achieve therotation and translation matrices. With the rotation and translationmatrices, the coordinates in the previous frames are projected into thecurrent frames and form a sequential track of the target. Since theimage alignment consumes most of the computation in the framework, thisstep is adaptively allocated in a cloud infrastructure.

The goal of the present invention is the development of a generalpurpose detection framework to increase the robustness of detection byutilizing the information from the optical flow generator (OFG) and anactive-learning histogram (AHM) matcher at the same time and inreal-time. The OFG and AHM processes are deployed in such a way thatthey act in a complementary manner by an intersection of each other torule out the erroneous data that may get detected by each processindividually and thus capture the potential and valuable moving targets.Since the background in the airborne video is generally moving, a properregistration process is necessary to make the data integration of thewhole image sequence possible.

As far as active-learning histogram matcher is concerned, the huecomponent of color in HSV (hue-saturation-value) space is used fortracking. A template hue histogram associated with a typical color isinitialized at the start of the working flow. From the start ofdetection to the start of tracking, the template hue histograms keepactively calibrating according to the slight color changes of thetargets. The histogram within a certain window is used as the targettracking feature in case the target was missing in the tracking and thisfeature makes detection and tracking robust. The active-learninghistogram matcher is reported in the following section.

As far as cloud-based framework is concerned, a cloud computing systemis built using Xenserver. A web portal in the cloud is provided for theuser. From this web portal, the user chooses algorithms, datasets, andsystem parameters such as the desired processing frame rate, maximumframe duration, etc. A controller in the cloud will then decide theamount of computing resources to be allocated to the task in order toachieve the user's requirement of performance. Inside the cloud, thereare several Virtual Machines (VMs) running on each physical machine.Each of these VMs is capable of running various detection, registration.and tracking algorithms. The computation consuming registrationalgorithm is usually run by several threads in one or more VMs. Theother major contribution is that a non real-time algorithm achievesreal-time performance based on the application of a cloud computinginfrastructure. The processing speed is improved by parallelimplementation and elastic resource usage of cloud computing.

Active-Learning Histogram Matcher

Referring to FIG. 1 and FIG. 2, the existing feature descriptorsincluding SIFT (Scale-Invariant Feature Transform), SURF (Speeded UpRobust Features) and Harris Corner pose feature challenges as far as thereal-time aspect is concerned due to their computational complexity. Tohave a more computationally efficient matching, the active-learninghistogram is adopted in the present invention. In the present invention,the color depth of hue component in HSV is set to 0 to 255 and they aredivided into 16 bars with the same step width, which means each bar has16 color bits. The two figures respectively represent two typicallycolored (e.g., Silver and Red) pixel distributions in Hue componentspace. The color distribution of the template histograms is consideredas 16-component vectors: (e.g., Silver [0, 20, 45, 38, 75, 18, 0, 0, 0,0, 0, 0, 0, 0, 0, 0] and Red [60, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 110]). The active-learning of the histogram matcher is applied as:C _(Template) =α*C _(Template) +β*C _(Candidate)  (1)As can be seen in equation (1), the calibration to the templatecomponent histogram is performed in each frame by the weighted linearaddition. The given C_(Template) is used to represent the currenttemplate histogram, C_(Candidate) can be considered for the strongcandidate histogram. α and β are two weighting factors of the linearadditive calibration. In the present invention α is chosen as 0.9 and βas 0.1 to update the current template Hue component histogram. Thecandidate histogram windows with the histogram are generated from theoptical flow field between two aligned consecutive frames.

Referring to FIG. 3, the three white blobs are generated from theoptical flow field after image alignment and the bounding rectangles areconsidered as candidate windows. The candidate windows with the mostsimilar Hue histogram are selected as the true target and thesecandidate Hue histograms are used to calibrate the current templatehistogram. As can be seen in the Figure, there are three strongcandidate windows in this frame. As expected, the candidate on the righthand side matched with the red template and the one on the left handside matched with the silver template, while the one in the middle isruled out because of the low similarity of the histogram with thetemplate histograms. In the present invention, the vectors consist of 16consecutive numbers of the bars in the histogram are considered thecolor feature distribution of the windows. The Dynamic Time Warping(DTW) algorithm is used as the histogram matcher as this algorithm iscapable of generating the dynamic distance between an unknown signal anda set of reference signals in a computational efficient manner. Thesample signal comes from the vector of the current template histogram.Note that the introduced silver and red template hue histograms in thepresent invention is for general purpose matching in the sense that itis applicable to other color histogram matching.

Referring to FIG. 4, an original RGB (red-green-blue) image for thevisible reference is presented to the system operator. Note that thegrayscale depiction of FIG. 4 is presented for the purposes of theinvention application.

Referring to FIG. 5 depicts an exemplary flowchart which describes howthe present invention works. In Step 501 the dataset of images in frameavailable in the cloud was selected at the beginning of the flow. InStep 502 a speech or voice command calls out the color desired of thetarget and was converted into a text input which is used to describe thefeature of the targets. The text designation of the target is convertedinto a Hue representation using the gamut colorspace naming convention.In the present invention, color distribution of the histogram is used torecognize and locate the targets of interest. In Step 503 once thecommand has been given, the detector as shown in the red box isinitialized. The first step in the detector is image alignment(Homography Matrices generation) and registration. This step is the mostcomputation-consuming step thus it is applied in cloud and performed ina computational efficient manner. In Step 504 based on the homographymatrices and then the registered frames, the optical flow field isgenerated. In Step 505, morphology processing which includes a Closingand an Opening operation using a 3×3 square structuring element isapplied on the optical flow field. The leftover blobs of the morphologyprocessing are considered as strong candidate contours of the target. InStep 506, the histogram templates of colors which are described in Step502 are used to match and recognize the valuable targets. In Step 507,the tracker is initialized. In the present invention, either aL1-tracker or a Compressive tracker are two options which are used totrack the targets of interest. In Step 508, based on the homographymatrices generated from the cloud infrastructure in Step 503, the priortracks of the objects are projected in current frame 510. In Step 509,in the event that the present invention cannot correctly locate thetarget, the speech command or text input is fed into the system toredefine or relocate the targets of interest.

Cloud Infrastructure Based Image Alignment

Referring to FIG. 7 depicts a high level view of a host in the cloudsystem. XenServer 709 serves as the Virtual Machine (VM) Manager, orhypervisor, of each physical server in the cloud. All components of thesame task have access to a shared storage 708 in the cloud. The user 701only interacts with the system through the Web GUI 702. The user'scomputing requests are delivered to controller 703 for furtherprocessing. The controller assigns an appropriate number of jobs to VMWorkers for each request. Each VM Worker runs one or more of theassigned tasks (detector 707. tracker 705 and register 706) and sendsthe results to the controller. The web GUI 702 will then display theprocessing result in real-time once the backend processing starts. TheMonitor 704 uses a database to store real-time performance of all tasksin the system. It also monitors the cloud's performance such as averageCPU (central processing unit) load and memory usage. The user chooseswhat metrics to display on the web GUI 702 and can call other visualanalytic tools such as a rendering on a ground plane, road network, or3-dimensional trajectory.

Interactive Tracking Module

The interactive tracking module provides the options to choose variousdetectors. registers, tracker algorithms and desired processing framerates as parameters before the system initializes. Each combination ofdetector, register, tracker and frame rate consists of a configurationof the system. Once a configuration is chosen by the user, the systemwill start to process the video sequence once the processing command isreceived from the web GUI. A target in the initial frame can also bemanually or audibly identified by the user as an alternative to adetector that selects the pixel boundary representing the target.

In the target detection and tracking scenario, the key components of thepresent invention perform the following tasks:

-   -   User. The user chooses configuration of the system, can verbally        designate a color of the object, and sends commands to the        system to initiate a task.

Web GUI. The web GUI communicates with the user by receiving inputcommands, displays processing results, and presents analytical systemperformance.

Controller. The Controller receives commands from the web GUI, makesdecisions on how many resources are needed to satisfy the requiredperformance, assigns jobs and tasks to virtual machines in the cloud,combines processing results of different components into resultantframes, calculates processing speed in real-time and informs the web GUIprocessing results.

Monitor. The Monitor collects performance metrics such as processingspeed and system load, and provides a query service for the web GUI whenthere is a need to display these metrics.

Virtual Machines (VMs). Each VM can act as a detector, register ortracker in the system. The actual roles a VM will perform are decided bythe Controller.

Voice Command Tracking

Referring to FIG. 8. the voice command tracking module is able toreceive commands from the user's speech in real-time. FIG. 8 shows theoverall data flow of this module. The system starts and enters “PlayVideo” 801, displaying the video sequence on the web GUI. At 802, theweb GUI keeps checking if the current frame is the last frame. If it is,the web GUI stops at the last frame, otherwise it listens to any voicesignal from the user at 803. The user triggers a voice command at anytime before the last frame is displayed. When speech signals arereceived from the user's microphone, the system first runs speechrecognition to identify the command. If a command is recognized, thecommand is sent to a detector running in the cloud, entering detect 804.The matcher 805 utilizes the speech command converted to text from whichthe text color designation is transformed into Hue space for matchingwith the detector processing the Hue of the pixels in the image. Whenthere is a matched target, a tracker will take over the execution at806, in coordinating with multiple registers determined by theController. The Controller will then combine processing results fromtrackers and registers and produce the resultant annotated videosequence 807. The web GUI will switch to display processed frames oncethe first processed frame is available. If there is no matched target inthe current frame, the web GUI will prompt a message to the user at 809and continue to display the next frame.

Referring to FIG. 9, the web GUI displays the system output when theuser triggers a voice command to track the silver car in the videosequence. There are three windows 901, 902 and 903 in FIG. 9. Window 901displays the original frames when no voice command is received. Once avoice command is triggered, the frames in this window will be replacedby processed frames with rectangle box on the target. On the right sideof the video frame is the optical flow window 902, synchronized with thecurrent video frame. Window 903 is the histogram of current frame usedby the detector to detect the target. The web GUI also supports a UserDefined Operating Picture (UDOP) reconfigurable display.

System Monitoring Module

Referring to FIG. 10, an instance of the System Monitoring Module or thedashboard on the web GUI is shown. Upon entering the dashboard page, theuser can choose one or more metrics, provided by the monitor, to bedisplayed. The selected charts are then created on the fly by the webGUI. Once all charts are created, the web GUI starts to query with themonitor and retrieve all data for each chart. In FIG. 10 the charts1001, 1002, and 1003 provide real-time monitoring metrics the userchose, updated every five seconds. Chart 1001 depicts the Cloud hostaverage CPU utilization, which is an indication of the fulfillment ofservice level agreement (SLA). Chart 1002 illustrates the real-timeprocessing performance, the frame per second (fps) processing rate. As abaseline, the fps of all algorithms running on a single machine areprovided. Chart 1003 shows the number of register threads/processesrunning when executing the task.

Target Track Generation

As the homography matrices of the frames are generated from the cloudinfrastructure based image alignment, the rotation and translationmatrices derived from homography matrices makes the integration of thewhole image sequence possible. Thus prior tracks of the objects areprojected into current frame. The homography matrix H, generated fromimage alignment, consists of 9 components.

$\begin{matrix}{H = \begin{bmatrix}h_{1} & h_{2} & h_{3} \\h_{4} & h_{5} & h_{6} \\h_{7} & h_{8} & h_{9}\end{bmatrix}} & (2)\end{matrix}$

Since the airborne transformation between frames are not only affine butalso projective, the geometric transformation can be represented by amatrix from

$\begin{matrix}{H = \begin{bmatrix}a & b & c \\d & e & f \\g & h & 1\end{bmatrix}} & (3)\end{matrix}$

Thus we have

$\begin{matrix}{\begin{bmatrix}x_{new} \\y_{new} \\1\end{bmatrix} = \frac{\begin{bmatrix}a & b & c \\d & e & f \\g & h & 1\end{bmatrix}\begin{bmatrix}x \\y \\1\end{bmatrix}}{\begin{bmatrix}g & h & 1\end{bmatrix}\begin{bmatrix}x \\y \\1\end{bmatrix}}} & (4)\end{matrix}$Equation (4) is an iterative process where

${x_{new} = {{\frac{{a\; x} + {b\; y} + c}{{g\; x} + {h\; y} + 1}\mspace{20mu}{and}\mspace{14mu} y_{new}} = \frac{{d\; x} + {s\; y} + f}{{g\; x} + {h\; y} + 1}}},{x_{new}\mspace{14mu}{and}\mspace{14mu} y_{new}}$represent the coordinates of the track generated in the current framewhich consist of the registered previous track x and y. In theHomography matrix

$H,\begin{bmatrix}a & b \\d & e\end{bmatrix}$represents the rotation matrix R, while

$\quad\begin{bmatrix}c \\f\end{bmatrix}$represents the translation matrix t. Rotation and translation matricesare called the matrix of extrinsic parameters. They are used to describethe camera motion around a static scene. [g h] gives the projectiveparameters, which describe the radial distortion and slight tangentialdistortion.

Referring to FIG. 6. the registered tracks are displayed in the currentframe, which record the motion curves of the objects of interest. In thepresent invention, two registration algorithms are applied: RANSAC(RANdom SAmple Consensus) and LMEDS (Least MEDian of Squares). Thetracks of RANSAC are shown as light dots, while the ones using LMEDS areshown in darker lines (one track for each target). At the beginning ofthe work flow, one of these algorithms is selected to align the imagesequences.

Having described preferred embodiments of the invention with referenceto the accompanying drawings, it is to be understood that the inventionis not limited to those precise embodiments, and that various changesand modifications may be effected therein by one skilled in the artwithout departing from the scope or spirit of the invention as definedin the appended claims.

What is claimed is:
 1. A method for video detection and tracking of atarget, comprising the steps of: defining said target, wherein in saidstep of defining further comprises the steps of: selecting a dataset oftarget image frames from a database; and selecting the desired color ofsaid target; converting said color to a template hue histogramrepresentation; initializing an image detector; performing target imageframe alignment and registration in which homography matrices aregenerated; generating an optical flow field; performing morphologyprocessing on said optical flow field so as to produce candidate targetcontours; matching said target contours to said template hue histogramrepresentation; initializing tracking of said target and generatingtarget tracks; aligning current said target image frame so as to form asequential track of said target; and when said target is not located,redefining said target.
 2. The method of claim 1, wherein said step ofgenerating an optical flow field further comprises generating an opticalflow field between two aligned consecutive target image frames.
 3. Themethod of claim 2, further comprising the step of identifying strongtarget candidates from the occurrence of optical flow blobs in saidoptical flow field.
 4. The method of claim 3, further comprising thestep of tuning said template hue histogram according to said strongtarget candidates.
 5. The method of claim 4, wherein said step of tuningsaid template hue histogram further comprises the steps of: adaptingcolor processing to the color characteristic of the light sourced underwhich said target image frames are captured; and updating said templatehue histogram in HSV color space to represent the color characteristicsof said target image frame being captured.
 6. The method of claim 5,wherein said step of updating further comprises the steps of: performinga weighted linear addition between said template hue histogram and a huehistogram corresponding to said strong target candidate; and collectingcolor pixels of said strong target candidate.
 7. The method of claim 1,wherein said step of performing current target image frame alignmentfurther comprises the steps of: generating rotation and translationmatrixes from said homography matrices; and projecting, using saidrotation and translation matrices, prior target frames of said targetinto a current target image frame.
 8. The method of claim 6, wherein thecolor depth of said hue histogram is divided into 16 segments over thevalue range of 0 to
 255. 9. The method of claim 8, wherein said step ofweighted linear addition further comprises the steps of: computing acurrent template histogram according to the expression:C _(Template) =α*C _(Template) +β*C _(Candidate) wherein C_(Template) isthe current template histogram; α is a first weighting factor; β is asecond weighting factor; and C_(Candidate) is a strong candidate for ahistogram window.
 10. The method of claim 9, wherein α is selected as0.9; and β is selected as 0.1.
 11. The method of claim 10, furthercomprising the step of selecting candidate histogram windows with themost similar hue histogram as a true target.
 12. The method of claim 7,wherein said step of performing target image frame alignment andregistration is performed in a cloud computing environment.
 13. Themethod of claim 12, and wherein said cloud computing environment isaccessed through a Graphical User Interface (GUI).
 14. The method ofclaim 13, wherein said cloud computing environment is further accessedthrough voice command.
 15. The method of claim 7, wherein saidhomography matrix is a 3 by 3 matrix represented by$H = {\begin{bmatrix}a & b & c \\d & e & f \\g & h & 1\end{bmatrix}\;.}$
 16. The method of claim 7, wherein said rotationmatrix R is represented by $\quad\begin{bmatrix}a & b \\d & e\end{bmatrix}$ said transformation matrix t is represented by$\quad\begin{bmatrix}c \\f\end{bmatrix}$ and projective parameters are represented by [g h]. 17.The method of claim 7, further comprising the step of computing thecoordinates of a track generated in a current frame, X_(new) andy_(new), according to$x_{new} = \frac{{a\; x} + {b\; y} + c}{{g\; x} + {h\; y} + 1}$ and$y_{new} = {\frac{{d\; x} + {e\; y} + f}{{g\; x} + {h\; y} + 1}.}$